Papers
5
Total Citations
82
H-Index
5
About
Linsen Song is a leading researcher in intelligent robotics, with a focus on meta-heuristic optimization, fault diagnosis, and advanced control systems. His work bridges the gap between nature-inspired algorithms and practical robotic applications, most notably through the development of the Wild Geese Migration Optimization (GMO) algorithm, which has garnered 37 citations for its novel approach to solving inverse kinematics in robots. Song has also made significant contributions to industrial robot reliability, proposing a multi-source data fusion method combined with channel attention convolutional neural networks for fault diagnosis—a paper that has already earned 18 citations. His hybrid improved Battle Royale Optimization algorithm (HBC) further demonstrates his ability to enhance computational performance for complex robotic tasks. In the realm of control theory, Song introduced a parallel network-based sliding mode tracking control to address uncertain dynamics in robotic manipulators, achieving robust stability under external perturbations. His recent work on digital twin-based parameter compensation for mobile robots highlights his commitment to real-world applications in high-risk environments. With a growing citation impact and a portfolio of innovative, cross-disciplinary solutions, Linsen Song is shaping the future of intelligent robotic systems.
Research Focus
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